ASP+POMDP: Integrating non-monotonic logic programming and probabilistic planning on robots
Shiqi Zhang, Mohan Sridharan, Forrest Sheng Bao · 2012
Mobile robots equipped with multiple sensors and deployed in real-world domains frequently find it difficult to process all sensor inputs, or to operate without any human input and domain knowledge. At the same time, robots cannot be equipped with all relevant domain knowledge in advance, and humans are unlikely to have the time and expertise to provide elaborate and accurate feedback. This paper presents a novel framework that addresses these challenges by integrating high-level logical inference with low-level probabilistic sequential decision-making. Specifically, Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is used to represent, reason with and revise domain knowledge obtained from sensor inputs and high-level human feedback, while hierarchical partially observable Markov decision processes (POMDPs) are used to automatically adapt visual sensing and information processing to the task at hand. Furthermore, a psychophysics-inspired strategy is used to merge the output of logical inference with probabilistic beliefs. All algorithms are evaluated in simulation and on wheeled robots localizing target objects in indoor domains.